Automatic presence detection is available for different purposes, such as building control, lighting adjustment or heating control, surveillance systems, burglar alarms, personal safety systems or car parking aids.
Even though accuracy is important for all these applications, different aspects of accuracy are important, and for different reasons. For a car parking aid, a false negative reading may lead to damage to your car, while a false positive reading is probably only a minor nuisance. A false positive reading in a personal safety system on the contrary, may lead to people being terrified. Repeated false positive readings in a surveillance system might have a “cry wolf” effect on a security officer assigned to investigate the surveyed area, leading to future true readings being ignored. False positive readings triggering heating or lighting systems will increase power consumption significantly. Most of the time these false readings have natural causes, but sometimes the functioning of the sensor is deliberately tampered with. Some applications are especially prone to tampering, e.g. surveillance or burglary alarms, and therefore it is desirable that sensors used in such applications be difficult to manipulate.
Different kinds of sensors are available for detecting presence. They are based on different physical principles, have different power requirements and have different strengths in terms of accuracy.
One of the most common approaches to detect presence is to use infrared detectors. A beam of IR light is emitted against a sensor, and the sensor will very accurately detect when the beam is being broken. Such sensors are often used e.g. in shop entrances, i.e. fairly narrow and confined passages. Detecting presence in a distributed space with a system based on this kind of IR sensors requires a multitude of beams to avoid the problem of false negative reading, where e.g. a burglar simply walks around, over or under the beams without breaking them.
Therefore presence detectors based on this technology are difficult to implement, unpractical, and power consuming, because of the power demands of the multitude of emitted IR light beams. In addition the system will register any object breaking a beam, and cannot distinguish a dead object from a human being. Used as a human presence detector, the IR beam detector will cause a high rate of false positive readings.
A Passive InfraRed detector (PID) however, uses the fact that all objects emit black body radiation, in practice infrared radiation, which is invisible to the human eye, but which can be detected by electronic devices designed for such a purpose. A PID measures IR light radiating from objects in its field of view. Apparent motion is detected when an infrared source with one temperature, such as a human, passes in front of an infrared source with another temperature, such as a wall.
The term passive in this instance means that the PID device does not emit an infrared beam but merely passively accepts incoming infrared radiation. Therefore it is significantly less power consuming than the active infra-red detector described above. However, a PID based system will potentially give an indication based on anything moving within its field of view, i.e. a high rate of false positive readings. Usually, a PID cannot distinguish between a human and other moving objects with any accuracy; if a moving object stops, and becomes immobile, the PID may lose track of it. Lastly, continuous bright light can saturate a PID sensor and render it unable to register further information. This feature makes it less reliable as a presence detector outdoors, and makes it relatively easy to manipulate.
Other approaches to detecting presence of humans include ultrasonic sensors.
Ultrasonic sensors work according to a principle similar to sonar and evaluate attributes of a target by interpreting the echoes from signals previously sent out. The time intervals between sending the signals and receiving the echoes is calculated and is then used to determine the distance to, and velocity of an object. The ultrasonic sensor is less good at detecting immobile objects. Further, an ultrasonic sensor based system may be prone to manipulation, because of the fact that surface shape, density or consistency of material covering an object can muffle the echo sufficiently to render the object invisible to the ultrasonic sensor.
A more accurate presence detection will require more complex methods, such as e.g.
audio or video analysis. Video analysis not only allows distinguishing between human beings and objects, but also allows distinguishing between individuals, as well as and tracking them, and detecting movements and activities. Another advantage of using a video camera is that immobile individuals can be detected. Audio analysis, where a microphone records sounds in the room and an algorithm trained to recognize typical sound patterns can be used to distinguish between different activities.
Audio/video scene analysis is a very complex process. In order to achieve robustness, algorithms have to be computationally demanding. The average power consumption needed to run such algorithms on a chip continuously is substantial.
As can be seen in the above examples, high accuracy typically comes with high power consumption. Thus there is a need for a presence detection system that maintains power consumption, while obtaining high detection accuracy.